{"id":"W2506778745","doi":"10.1002/jbio.201600021","title":"In‐vivo multispectral video endoscopy towards in‐vivo hyperspectral video endoscopy","year":2016,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg; Erlangen Graduate School of Advanced Optical Technologies; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Multispectral image; Hyperspectral imaging; Endoscopy; In vivo; Artificial intelligence; Computer science; Support vector machine; Cancer detection; Cancer; Pattern recognition (psychology); Computer vision; Pathology; Medical physics; Radiology; Medicine; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007868737,0.0003639396,0.0008884072,0.000665153,0.00006089594,0.000042525,0.0002671005,0.0002680198,0.0005473794],"category_scores_gemma":[0.0003309717,0.0002594775,0.0004548176,0.0007019895,0.0001328913,0.0004492314,0.00005271128,0.000741646,0.00002161194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096524,"about_ca_system_score_gemma":0.0006697394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004776621,"about_ca_topic_score_gemma":0.0004169694,"domain_scores_codex":[0.9969171,0.00009926275,0.00108421,0.0004260819,0.0007502313,0.0007231372],"domain_scores_gemma":[0.9983267,0.0001984722,0.0004897144,0.0003803108,0.0002299808,0.0003748818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01172847,0.0003702353,0.00743699,0.00006053126,0.00009001933,0.001206554,0.0004402422,0.0000213021,0.9730022,0.00007510114,0.003008352,0.002560003],"study_design_scores_gemma":[0.01152109,0.005234607,0.003433637,0.0008331842,0.0001088589,0.002142636,0.0002235233,0.0002243242,0.9541858,0.0002869364,0.02145889,0.0003464533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882447,0.002021497,0.0002960656,0.003446265,0.001848575,0.0003758507,0.00002845949,0.00004853038,0.003690021],"genre_scores_gemma":[0.9873545,0.002440055,0.007705628,0.0009137187,0.0006739078,0.00001824218,4.356259e-7,0.00005699816,0.0008365303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01881634,"threshold_uncertainty_score":0.9999858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380985685489416,"score_gpt":0.2762663258714457,"score_spread":0.2624564690165516,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}